Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Nelineārā OLS (Nelineārā mazāko kvadrātu summa)× | Nelineārā vispārinātā mazāko kvadrātu metode (NGLS)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1974–1987 | 1975 |
| Autors≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Gallant (1975); extended by Davidson & MacKinnon |
| Tips≠ | Nonlinear regression estimator | Nonlinear estimator |
| Pirmavots≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 | Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600 |
| Citi nosaukumi | nonlinear least squares, NLS, NLLS, nonlinear regression | NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS |
| Saistītās≠ | 5 | 2 |
| Kopsavilkums≠ | Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal. | Nonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates. |
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